Morphological segmentation with tiling light sheet microscopy to quantitatively analyze the three-dimensional structures of spinal motoneurons

Morphological segmentation with tiling light sheet microscopy to quantitatively analyze the three-dimensional structures of spinal motoneurons | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Morphological segmentation with tiling light sheet microscopy to quantitatively analyze the three-dimensional structures of spinal motoneurons Huijie Hu, Dongyue Wang, Yanlu Chen, Liang Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5758234/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Spinal motoneurons control muscle fibers contraction and drive all motor behaviors in vertebrates. Although spinal motoneurons share the fundamental role of innervating muscle fibers, they exhibit remarkable diversity that reflects their specific identities. Defining the morphological changes during postnatal development is critical for elucidating this diversity. However, our understanding of the three-dimensional (3D) morphology of spinal motoneurons at these stages remains limited, largely due to the lack of high-throughput imaging tools. Using tiling light sheet microscopy combined with tissue clearing methods, we imaged motoneurons of the lateral and median motor column in the cervical and lumbar cord during postnatal development. By analyzing their soma size, we found that motoneurons innervating the upper limbs differentiate into two subpopulations with distinct soma size by postnatal day 14 (P14), while differentiation of motoneurons innervating the lower limbs is delayed. Furthermore, coupling adenovirus labeling with 3D volumetric reconstruction, we traced and measured the number and lengths of dendrites of flexor and extensor motoneurons in the lumbar cord, finding that the number of dendrites initially increases and subsequently declines as dendritic order rises. Together, these findings provide a quantitative analysis of the 3D morphological changes underlying spinal motoneuron diversity. Cellular & Molecular Neuroscience Tiling light sheet microscopy Tissue clearing methods Spinal motoneurons 3D volumetric reconstruction Soma size Dendritic arborization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background All motor commands, whether voluntary or involuntary, are ultimately integrated by spinal motoneurons (SpMNs) (Arber and Costa, 2018 ; Arber, 2012 ). SpMNs receive neural projections from descending pathways, spinal interneurons, and peripheral sensory inputs (Côté et al., 2018 ; de Carvalho and Swash, 2023 ), transmitting all signals to muscle fibers via neuromuscular junctions (Stifani, 2014 ). Motor behaviors depend on the coordinate recruitment of different muscle groups, each activated by a specialized set of SpMNs (Agalliu et al., 2009 ). A distinct anatomical correlation exists between SpMNs and muscle fibers, enabling the temporal and spatial synchronization of muscle groups under the control of spMNs (Kernell, 2006 ). To facilitate the production of varying levels of force output in response to diverse movement scenarios, motor pools typically comprise three distinct types of SpMNs (Bączyk et al., 2022 ; Dasen, 2022 ; Simon et al., 1996 ; Kernell, 2006 ; Stifani, 2014 ). Establishment of cellular diversity during development is essential for SpMNs to execute precise movements. Over the past decades, transcription factor and epigenetic-mediated MN development has been extensively studied in vivo (Agalliu et al., 2009 ; Ashrafi et al., 2012 ; Liau et al., 2023 ; Mendelsohn et al., 2017 ; Dasen et al., 2003 ; Bulajić et al., 2020 ). However, anatomical studies of MNs during spinal cord development were limited to 2D histological assessment (Kanjhan et al., 2016b ; Kanjhan et al., 2016a ; Fukuda et al., 2020 ). It remains unclear how different types of MNs are organized within the spinal cord and how the spatial organization changes during development. As regards imaging, several fluorescence microscopy techniques are available for obtaining volumetric data of whole tissues (Zhong et al., 2021 ; Pesce et al., 2022 ). However, while the acquisition speed is an inherent limitation for imaging large-volume tissues, tiling light sheet microscope (TLSM) overcomes this by enabling rapid acquisition of cleared samples. TLSM enables high throughput 3D imaging of centimeter-scale cleared and expanded biospecimens with spatial resolutions ranging from microns to sub-hundred nanometers (Chen et al., 2020 ). The combination of tissue clearing and TLSM techniques provided a powerful tool for investigating 3D morphological diversity and spatial organization of MNs during postnatal development. MNs are organized hierarchically, with distinct columns aligned along the rostrocaudal axis of the spinal cord, each targeting specific peripheral structures(Tosney et al., 1995 ). The median motor column (MMC) and lateral motor column (LMC) neurons are essential for mice, as they coordinate axial muscle activity to maintain posture and balance while enabling precise limb movements critical for locomotion and other motor behaviors (Patani, 2016 ; Nicolopoulos-Stournaras and Iles, 1983 ; Gutman et al., 1993 ; Dasen, 2022 ). Here, we conducted quantitative analysis of the changes in 3D soma size of MMC and LMC neurons in the cervical and lumbar cord at five ages (P1, P7, P14 and P56). In addition, dendrites integrate synaptic inputs while minimizing metabolic costs by extending to existing or potential synaptic targets, making the understanding of the dendritic arborization of spinal motoneurons essential (Stuart and Spruston, 2015 ; Magee, 2000 ). Sparse labeling is an important aspect of tissue preparation for dendrite tracing and analysis (Bloss et al., 2018 ; Lin et al., 2018 ). Over the last two decades, adenovirus (AdV) vectors have become powerful tools for labeling single neuron and treating diseases of central nervous system (CNS) (Salinas et al., 2009 ; Andrew Paul Tosolini and Morris, 2016a ). In this study, we found that adenoviral injections produced robust fluorescent expression in MNs with high spatiotemporal resolution, effectively labeling retrogradely. We used AdV via intramuscular injection to label MNs innervating paired flexor and extensor muscles at P4, P14, and P56. Accurate 3D quantification of soma and dendrites is essential for understanding the normal and pathological neuronal function of MNs. However, traditional quantification has relied on slow, labor-intensive methods like manual counting, which are low-throughput and unsuitable for large samples (Herculano-Houzel and Lent, 2005 ; Fukuda et al., 2020 ). Specifically, manual editing will vary depending on the complexity of the image data. This article presented semi-automatic segmentation methods for objective morphological analysis by providing image acquisition parameters with Amira. These protocols will serve as a valuable reference for scientists aiming to quantify and characterize neural structures in the CNS. Collectively, we imaged the MMC and LMC neurons in the cervical and lumbar cord during postnatal development as well as MNs inverting paired flexor and extensor muscles. We further presented protocols for 3D volumetric analysis of soma size and dendritic arborization. These morphological analyses offer valuable insights into the diversity of MNs during postnatal development. Results Soma segmentation with Amira The TLSM was employed in conjunction with the CUBIC-L tissue clearing method (Matsumoto et al., 2019 ) to image the spinal cord of ChAT-eGFP mice at five stages (P1, P7, P14, P28, and P56). A more detailed description of the alignment and operation of TLSM can be found in the previous publication (Feng et al., 2021 ). Register and merge the images from adjacent sample volumes with Amira (Feng et al., 2021 ). The high density of MNs in the spinal cord of young mice (e.g., at P1 and P7) presents a challenge for automated single-cell segmentation due to the minimal spacing between MNs. To address this issue, we developed a semi-automated soma segmentation workflow based on deep learning algorithms using the commercial software, Amira. All operations were performed using Amira commands. The Extract Subvolume command was used to extract a small image block with a data size of approximately 30 megabytes (Fig. 1 a, 1 b). Then, we used the Median Filter command to erode the signal in the dendrites and axons of MNs. The filter can remove tiny structures, like dendrites and axons, without affecting the boundaries of large components, such as somas (Fig. 1 c). Next, the Hysteresis Thresholding command was used to convert the grayscale image into a binary image, represented by a blue mask (Fig. 1 d). However, as shown by the red arrows in Fig. 1 d, this process failed to accurately extract every soma. To enhance the accuracy of soma region extraction, a deep learning command ( DL Training-Segmentation 3D ) was employed. The parameter settings of deep learning command were presented in Table S1. The deep learning-trained model was used to predict the soma regions (Fig. 1 e) in the original grayscale image, achieving greater accuracy compared to the extraction results obtained using the Hysteresis Thresholding command. The Image Gradient command facilitated the delineation of boundaries between the adjacent MNs (Fig. 1 f). Figure 1 h and 1 i demonstrated the application of the deep learning-trained model for predicting soma in 3D samples, with the red mask indicating the predicted soma contours. Additionally, the 3D reconstructed soma mask was shown in Figure j. Figure S1 illustrated the detailed workflow for counting the number of MNs, while the signals extracted therein serve as seeds in the subsequent watershed segmentation procedure, enabling comprehensive and accurate segmentation. We next applied the Marker-based Watershed inside Mask command to segment individual MN. Each MN was labeled by unique random color, as shown in Fig. 1 g. The parameters of the above commands were provided in Figure S2 for reference. The generation of accurate soma masks is the most critical step in the whole semi-automated soma segmentation workflow. Traditional threshold segmentation struggles to accurately extract soma regions from the entire 3D image due to the high density of MNs in ChAT-eGFP mice and the complexity of dendritic branching. The deep learning module in Amira software significantly enhanced the accuracy and efficiency of soma boundary extraction. In addition, as the spinal cord was expanded by ~ 1.2 times in each dimension after clearing, the real soma size was 1.728 (1.2 3 ) times smaller than the calculated soma size. Soma size changes of MMC and LMC neurons during postnatal development MN subtypes are distinguished by their anatomical position, functional properties, connection specificity, and molecular profiles (Dasen, 2022 ). The diversity of MNs is a functional necessity for the proper execution and control of movement during postnatal development. Mature MNs exhibit variations in soma size. It is not feasible to differentiate MNs based solely on soma size. However, the observed differences in soma size among MNs can be utilized as a supplementary indicator of differentiation. In previous studies, the soma size of MN was typically assessed by measuring the maximum cross-sectional area of MN in spinal cord slice. Changes in the 3D soma size and the spatial distribution of MN subtypes within the spinal cord during postnatal development remain poorly characterized. We identified LMC neurons in the cervical (C5–T1) and lumbar (L1–L6) cord regions by correlating vertebral anatomy with spinal cord segments in mice (Figure S3) (Stifani, 2014 ; Dasen and Jessell, 2009 ; Harrison et al., 2013 ). MMC neurons in these regions were also included in the analysis. The MMC and LMC columns were identified by their spatial locations within the spinal cord, and their distinct boundaries were clearly revealed through 3D image rendering (Video S1). Figure 2 a-c and Video S2 display the cervical and lumbar spinal cord of a P1 ChAT-eGFP mouse, along with the reconstructed LMC and MMC neurons. The transverse planes of these two regions were presented in Fig. 1 d. The top was the cervical region, and the bottom was the lumbar region. Figure 1 e presented the transverse planes of the reconstructed LMC and MMC neurons. The volumetric changes of MNs during postnatal development were characterized by quantifying the soma size of ChAT-eGFP mice at five ages (P1, P7, P14, P28, P56). A total of 1,049 (P1), 870 (P7), 889 (P14), 783 (P28), and 945 (P56) MNs were reconstructed in the cervical MMC. Figure 3 a showed the histograms of soma size distributions in the cervical MMC of P1, P7, P14, P28, and P56 ChAT-eGFP mice. Similarly, 3,798 (P1), 3,688 (P7), 3,704 (P14), 3,516 (P28), and 3,553 (P56) MNs were reconstructed in the cervical LMC of ChAT-eGFP. Figure 3 b demonstrated the histograms of soma size distributions in the cervical LMC of P1, P7, P14, P28, and P56 ChAT-eGFP mice. In the lumbar MMC, 498 (P1), 382 (P7), 375 (P14), 504 (P28), and 594 (P56) MNs were reconstructed. Figure 3 c depicted the histograms of soma size distributions in the lumbar MMC of P1, P7, P14, P28, and P56 ChAT-eGFP mice. A total of 2,898 (P1), 2,482 (P7), 2,463 (P14), 2,762 (P28), and 2,750 (P56) MNs were reconstructed in the lumbar LMC. Figure 3 d presented the histograms of soma size distributions in the lumbar LMC of P1, P7, P14, P28, and P56 ChAT-eGFP mice. The soma size (volume3d) of MNs in each motor column during postnatal development were presented in Table S2. In the cervical MMC of P1 and P7 mice, the soma size distribution histogram exhibited a single peak. By P14, this distribution evolved to display two distinct peaks, corresponding to the differentiation of MNs into two subgroups. To analyze the soma size distribution histograms of the two MN subgroups, we applied the Gaussian fitting algorithm in Origin software to fit the histogram data for each subgroup. In the P14, P28 and P56 mice, MNs with smaller soma sizes accounted for 32.6%, 34.6% and 35.2%, respectively (Fig. 3 a, indicated by red arrows). Similarly, in the cervical LMC of P1 and P7, the soma size distribution was represented by a single group. At P14, MNs with smaller soma sizes constituted 31.4% of the total population. In P28 and P56 mice, the proportion of smaller-sized MNs was 25.9% and 27.1%, respectively (Fig. 3 b, indicated by red arrows). In the lumbar MMC of P1 to P14 mice, the soma size of MNs was distributed within a single group. In the mice at P28 and P56, MNs with smaller soma sizes presented 28.8% and 30% (Fig. 3 c, indicated by red arrows), respectively. Additionally, the soma size of MNs in the lumbar LMC of P1 to P14 was represented by a single group. In the P28 and P56 mice, MNs of smaller soma size comprised 31.1% and 31.3% (Fig. 3 d, indicated by red arrows), respectively. Spatial distribution of putative gamma and alpha MNs in the MMC and LMC Friese et al. employed a combined approach of tissue sectioning and immunofluorescence staining techniques to analyze the maximum cross-sectional area of 800 MNs in the lumbar spinal cord of P21 wild-type mice (Friese et al., 2009 ). The researchers identified two distinct populations of MNs: Err3-positive and NeuN-negative MNs (widely recognized as the selective markers for γMNs) and Err3-negative and NeuN-positive MNs (widely recognized as αMNs). The researchers also discovered that Err3-positive and NeuN-negative MNs (putative γMNs) constitute 31% of the total population. Our observation was in accordance with the findings of Friese et al. We speculated that the subgroup of MNs with smaller soma sizes corresponds to γMNs, while the larger one corresponds to αMNs. We identified the spatial distribution patterns of putative γMNs and αMNs in the mature spinal cord using the results of Gaussian fitting. The x-coordinate of the intersection point between the two fitting curves defined the threshold that separates the putative γMNs (soma size smaller than the threshold) and αMNs (soma size larger than the threshold). In the cervical LMC of P56 ChAT-eGFP, putative γMNs are represented by blue spheres, and putative αMNs were represented by red spheres (Fig. 4 a, 4 b). These spheres were generated through 3D rendering using Amira software. Next, putative γMNs and αMNs in the cervical MMC of P56 ChAT-eGFP were also showed in blue spheres and red spheres (based on Fig. 3 b), as illustrated in Fig. 4 a and Fig. 4 d. In the same way, the putative γMNs and αMNs in the LMC and MMC of the lumbar cord were represented as blue and red spheres, as shown in Figure S4. Through 3D rendering of the spatial distribution of putative γMNs and αMNs, we observed an interesting phenomenon: although smaller MNs (putative γMNs) account for approximately 30% of the total population, their spatial distribution was not uniformly proportional to this percentage (Fig. 4 c, 4 e). While the spatial distribution pattern of putative γMNs and αMNs was preliminarily predicted through soma reconstruction and 3D rendering, confirming these observations with immunohistochemical analyses (e.g., detecting Err3 and NeuN) remains a key step for future exploration. Dendrites extraction with Amira In order to investigate the dendritic arborization patterns of MNs at the single-cell level during development, we retrogradely labeled the MNs innervating the tibialis anterior muscle (TA, flexor) and the gastrocnemius lateralis muscle (GL, extensor) with AdV at P4, P14, and P56 (Figure S5a–f). Multiple site injections of AdV-eGFP and AdV-tdTomato were administered along the three-dimensional distribution of each target muscle’s motor endplate (Xu et al., 2021 ). A mixture of AdV and 1% Fast Green was used to aid in visualization (Figure S5g, top). After 72 hours, the spinal cord was dissected and the fluorescent signals in the target muscles were observed under a fluorescence stereomicroscope. The dissected spinal cord then underwent tissue clearing and was imaged using TLSM (bottom of Figure S5g, 5a). We employed a quantitative analysis of the spatial distribution of MNs innervating TA and GL muscles at P4, P14, and P56. As age increased, MNs were located progressively farther from the lateral and ventral borders of the spinal cord (Figure S5h–j). A semi-automatic method for dendritic tree extraction was developed using Amira (Video S3). This method involved reconstructing dendritic arborization and quantifying the number and length of dendrites. First, the Structure Enhancement Filter (Rod Model) command was used to highlight tubular structures (Fig. 5 b), while the Structure Enhancement Filter (Ball Model) command was applied to enhance spherical structures (Fig. 5 c). Next, the Interactive Thresholding command converted the grayscale images into binary images, visualized as blue masks (Fig. 5 d). Subsequently, the Marker-based Watershed inside Mask command assigned distinct labels to each MN, with each label displayed in a different color (Fig. 5 e). The Centerline Tree command then extracted the centerline of the segmented MNs, producing the dendritic branch skeleton (Fig. 5 f). Finally, manual tracing and editing were performed to ensure the complete and accurate extraction of the distal dendrites (Fig. 5 g). Dendritic branching patterns of MNs that innervate the flexor and extensor muscles at P4, P14 and P56 We reconstructed the 3D skeleton of dendrites of 3 motor neurons from the TA (Figure S6a) and GL (Figure S6b) motor pools at P4, P14 and P56. The reconstructed neural surface included the soma, dendrites, and axon (Fig. 6 a, 6 d), with dendritic branches color-coded by branch order using unique random colors for each order (Fig. 6 b, 6 e). To evaluate changes in dendritic complexity during development, we conducted a Sholl analysis (Fig. 6 a, 6 d) (Sholl, 1953 ), which revealed that the number of intersections between the dendrites and concentric circles initially increased and then decreased (Fig. 6 g). By integrating the Sholl analysis results with dendritic order information, we found that the region of highest dendritic density was associated with the 4th or 5th dendritic order (Fig. 6 h). We next quantified the mean length of the first six dendritic orders in both TA and GL MNs at each developmental stage (Fig. 6 i). In TA MNs, the mean length of each dendritic branch increased from P4 to P14, with no statistical difference detected between P14 and P56 (Fig. 6 i, top). In contrast, GL MNs displayed a continuous increase in the mean length of each dendritic branch from P4 to P56 (Fig. 6 i, bottom). In addition, it has been demonstrated that dendrites increase their probability of connecting with target synapses by increasing their curvature (Stepanyants et al., 2004 ; Rothnie et al., 2006 ). we also measured tortuosity-defined as the ratio of curve length to chord length (Fig. 6 c, 6 f)- in the first six dendritic orders of TA and GL MNs at P4, P14, and P56. We compared the P1 vs. P14 vs. P56 groups. No statistical differences in tortuosity were observed during development (Fig. 6 j). Discussion In this study, we started with utilizing a combination of TLSM and CUBIC-L to visualize the MNs of ChAT-eGFP mice during postnatal development. The imaging and tissue clearing techniques provided rapid acquisition of large-volume samples with submicron resolution and obviate the need for physically sectioning. Then, we developed a protocol to effectively segment soma from large-volume and high-throughput samples using Amira. Commercial software typically provides standardized processing procedures, which enhance the reproducibility. MNs undergo a differentiation process during development, resulting in the emergence of three subtypes: αMNs, βMNs and γMNs (MANUEL and ZYTNICKI, 2011 ). This regulation is crucial for the maintenance of muscle tone, the adjustment of muscle reflexes, and fine motor control. Previous studies have employed immunofluorescence and tissue sectioning techniques to analyze the cross-sectional area of αMNs and γMNs. The histogram of the cross-sectional area of the soma revealed the presence of two distinct groups. The smaller group was identified as putative γMNs, constituting approximately 30% of the total (Friese et al., 2009 ; Kang et al., 2024b ; Shneider et al., 2009 ). Our study revealed that MNs in the cervical and lumbar regions also differentiated into two groups with distinct soma sizes around P14, which was consistent with the results of the aforementioned studies. We speculated that the alterations in MN soma size during development may serve as an indicator of differentiation. Previous studies have indicated that the muscle groups of the forelimbs develop slightly earlier than those of the hindlimbs during the initial few days after birth (Zhu and Tabin, 2023 ; Martin, 1990 ). For instance, at P11, the phosphorylation levels of specific proteins, such as Pak1 and Pak2, exhibit disparities between the forelimb and hindlimb muscles (Joseph et al., 2019 ). This early developmental advantage endows the forelimbs with greater robustness when neonatal mice commence movement and exploration of their surrounding environment. The present study analyzed the 3D soma size of MNs in the cervical region and revealed that MNs innervating the forelimbs differentiate into two distinct groups with varying soma sizes by P14. However, the differentiation of MNs in the lumbar region was delayed until after P14. This observation indicated that the maturation and differentiation of MNs innervating the hindlimbs occur at a later stage than those innervating the forelimbs. Additionally, our findings revealed that the distribution of putative γMNs and αMNs is not uniform. In certain regions, putative γMNs are more abundant, while in others, putative αMNs are more prevalent. Although no further validation was performed, this finding suggested that the proportion of γMNs and αMNs may vary depending on the muscle fibers they innervate. It’s practicable to inject retrograde tracers into the target muscle, which could allow for a predictive analysis of the proportion of γMNs and αMNs by examining the soma size. We speculated that the function and influence on the ALS-like pathologies of the particular muscle could be further inferred from the proportion of γMNs and αMNs in the corresponding motor pool. This approach may also provide a theoretical basis for understanding how the motor system efficiently synchronizes various motor pools and for developing more effective exercise strategies. It was observed that MNs retrogradely labeled with AdV exhibited robust fluorescent expression, while the distribution of fluorescence signals was uneven. In particular, the fluorescence intensity in the soma region differed by a factor of 2–3 compared to the apical dendrite region. To address this problem, we employed extensive manual tracing to extract the complete dendritic arborization. The analysis demonstrated that the number of dendritic branches of MNs, regardless of whether they innervate flexors or extensors, tended to increase and then decrease. This indicates that the number of branches is less near the soma and proximal dendrites, with the majority of branches situated in the mid-dendritic region. It is noteworthy that the length of dendrites of MNs increases with age, and the pattern of dendrites constructed by P4 is similar to that of mature adults. The intricate structure of dendrites is primarily a consequence of their function in integrating synaptic inputs while simultaneously minimizing metabolic costs. Furthermore, comprehensive understanding the dendritic structures of MNs can also facilitate the development of efficient mathematical models of neural networks for motor control. In summary, this study provided a method for capturing the 3D morphology of MNs during postnatal development by combining TLSM imaging system, CUBIC-L tissue clearing protocol, and AdV labeling. Moreover, this study established highly reproducible image analysis methods to quantitatively analyze the morphological changes of MNs during postnatal development in two aspects: differentiation of soma size and dendritic architecture. The findings provide new insights into the maturation, differentiation, and functional properties of MNs, allowing a deeper understanding of the motor system during development. Limitations The achievement of high-resolution imaging of MNs in the whole spinal cord requires the handling of tens to hundreds of terabytes of data. Despite the establishment of a semi-automatic soma segmentation method based on deep learning, the computational process remains time-consuming and requires high-performance workstations. Consequently, in the initial stages of our investigation, the changes in soma size were restricted to the cervical and lumbar LMC and MMC of a single mouse per age group. This limitation may affect the generalizability of our findings, as the data from a single mouse may not fully represent the variability and complexity observed in a broader population. Besides, how can γMNs be differentiated before the clear bifurcation of their soma size into two distinct groups? This question cannot be answered on the basis of morphological changes alone. Further explorations involving specific molecular markers and electrophysiological properties of αMNs, βMNs and γMNs are required. Over the past decade, numerous molecular markers for γMNs have been identified (Zuccaro et al., 2021 ; Kang et al., 2024a ). However, it should be noted that many of these markers can be expressed in both αMNs and βMNs. Combing multiple molecular markers is essential. In examining the dendritic arborization of MNs, the TA and GL, which are superficial muscles, were labeled. The present study did not examine the dendritic branching patterns of MNs that innervate deeper muscles. This introduces a limitation, as the structural and functional characteristics of MNs may differ between the superficial and deep muscles. Furthermore, the intricate and compact structure of MN distal dendrites, necessitated substantial manual calibration to ensure accurate segmentation. The labor-intensive nature of this process resulted in a reduction in the overall throughput of data analysis, thereby limiting the number of MNs that could be selected for dendrite analysis to only three per age group. Furthermore, the dendrite tracing approach only extracted the centerline of the dendrites. The centerline extraction algorithm of Amira identifies the highest intensity path within a dendrite, rather than capturing the full grayscale signal present in the original image. As a result, this method does not yield information regarding the diameter, surface area, and volume of a dendrite. The diameter of a dendrite has a direct effect on its electrical conductivity. Measuring the diameter of a dendrite requires the application of additional analysis algorithms. Declarations Ethics approval and consent to participate The collection of mice tissues used in this study was approved by the Institutional Animal Care and Use Committee of the Westlake University (Approval No: 19-035-GL). Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Funding This work was supported by Westlake Education Foundation, the National Natural Science Foundation of China (32150015) and the Zhejiang Province Natural Science Foundation (LR20C070002). 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P., & Morris, R. (2016a). Targeting Motor End Plates for Delivery of Adenoviruses: An Approach to Maximize Uptake and Transduction of Spinal Cord Motor Neurons. Scientific Reports , 6(1), 33058. doi:10.1038/srep33058. Tosolini, A. P., & Morris, R. (2016b). Targeting Motor End Plates for Delivery of Adenoviruses: An Approach to Maximize Uptake and Transduction of Spinal Cord Motor Neurons. Sci Rep , 6, 33058. doi:10.1038/srep33058. Xu, J., Xuan, A., Liu, Z., Li, Y., Zhu, J., Yao, Y., et al. (2021). An Approach to Maximize Retrograde Transport Based on the Spatial Distribution of Motor Endplates in Mouse Hindlimb Muscles. Front Cell Neurosci , 15, 707982. doi:10.3389/fncel.2021.707982. Zhong, Q., Li, A., Jin, R., Zhang, D., Li, X., Jia, X., et al. (2021). High-definition imaging using line-illumination modulation microscopy. Nature Methods , 18(3), 309-315. doi:10.1038/s41592-021-01074-x. Zhu, M., & Tabin, C. J. (2023). The role of timing in the development and evolution of the limb. Front Cell Dev Biol , 11, 1135519. doi:10.3389/fcell.2023.1135519. Zuccaro, E., Piol, D., Basso, M., & Pennuto, M. (2021). Motor Neuron Diseases and Neuroprotective Peptides: A Closer Look to Neurons. Front Aging Neurosci , 13, 723871. doi:10.3389/fnagi.2021.723871. Methods Animal preparation The collection of mice tissues used in this study was approved by the Institutional Animal Care and Use Committee of the Westlake University (Approval No: 19-035-GL). All mouse lines were kept on C57BL/6J background and were housed in a standard 12:12 light-dark cycle. C57BL/6J was purchased from Shanghai Jihui Laboratory Animal Care Co., Ltd..ChAT-eGFP (B6.Cg-Tg(RP23-268L19-EGFP)2Mik/J, Stock No: 007902) was kindly provided by Dr. Liang Wang from Zhejiang University. Tracer preparation An expression cassette comprising the cytomegalovirus promoter and the cDNA encoding enhanced green fluorescent protein (eGFP) packaged into adenovirus serotype 5 was purchased from OBiO Technology（Shanghai）Corp., Ltd.. The viral titer was 1.58 × 10 11 pfu/ml. The solutions were stored at -80℃ until use. Intramuscular injections The location of injection sites based on the 3D distributions of motor endplate in the tibialis anterior and gastrocnemius lateralis, according to the published literatures (Xu et al., 2021; A. P. Tosolini and Morris, 2016b). The mice were deeply anaesthetized, and the right hind limb was secured with medical tape, shaving the tibialis anterior and gastrocnemius lateralis muscle regions. In the mice at P1, three sites were injected with an injection depth of 0.1-0.2 mm; In the mice at P11, five sites were injected with an injection depth of 0.3-0.6 mm; In the mice at P51, contained seven injection sites with an injection depth of 0.6-1.2 mm. Each site was injected with 0.5 μl of AdV. Spinal cord harvest After the intramuscular injections, the mice were kept for 3 days to allow for optimal retrograde transport of neuronal tracer. Then, mice were deeply anaesthetized and perfused with 37℃ saline followed by 4℃ 4% paraformaldehyde. The mouse spinal cord was dissected following a previously reported protocol (Kennedy et al., 2011). The dissected spinal cord was loosely wrapped with a 200 μm Nylon mesh and bound to a perforated Teflon plate with surgical sutures so that the spinal cord can maintain the straight shape through the sample preparation process. The bound spinal cord was placed in 4% PFA overnight at 4°C with gentle shaking. Preparation of cleared and expanded mouse spinal cord samples The preparation of cleared spinal cord samples was referenced from CUBIC-L protocol (Matsumoto et al., 2019; Tainaka et al., 2018). Preparation of the cleared spinal cord: First, the fixed spinal cord was immersed in the delipidation solution (10 wt% N-butyldiethanolamine, 10 wt% Triton X-100 and 80 wt% dH2O) at 37 °C for three days with gentle shaking. The delipidation solution was replaced every 24 hours. Next, the delipidated spinal cord was RI matched by immersing the spinal cord in the RI matching solution of RI ~1.49 (25 wt% urea, 22.5 wt% sucrose, 22.5 wt% antipyrine, 10 wt% triethanolamine) at 25°C for two days with gentle shaking. The RI matching solution was replaced every 24 hours. Finally, the RI matched spinal cord was embedded in 2% agarose gel made with the RI matching solution. The cleared and embedded spinal cord was mounted on the sample holder by attaching the sample to the magnets of the sample holder and imaged with the imaging system using Silicone oil as the imaging buffer (Feng et al., 2022). Microscopy The configuration of our imaging system was described in our previous publications (Chen et al., 2020). It is capable of imaging centimeter-scale cleared tissues with micron-scale to submicron-scale spatial resolution by using real-time optimized tiling light sheets and detective objectives with various numerical apertures (Olympus XLSLPLN10XSVMP, Nikon Plan Apo 10x Glyc, or Nikon CFI90 20XC Glyc). The resolving ability can be improved to ~70 nm in combined with tissue expansion. The microscope conducts multicolor imaging sequentially for up to 5 colors with the excitation wavelengths of 405 nm, 488 nm, 515 nm, 561 nm, and 638 nm. Image analysis Image processing, registration and merging protocols were described in our previous publications (Feng et al., 2021). Soma segmentation and dendrite tracing were conducted using Amira semiautomatically, and the methodologies are described in detail in Figure 1 (soma segmentation) and 5 (dendrite tracing). Additional Declarations The authors declare no competing interests. Supplementary Files DocumentS1cellregeneration.docx Supplemental information TableS1.xlsx Table S1. The parameter settings of the deep learning command for soma segmentation. TableS2.xlsx Table S2. The soma size (volume3d) of MNs in each motor column of cervical and lumbar cord at P1, P7, P14, P28 and P56. 49831735908282.mp4 Video S1. The 3D image rendering of the MMC and LMC columns with a clear boundary of P56 ChAT-eGFP. Related to Figure 1. 49841735908295.mp4 Video S2. The 3D image rendering of the cervical cord with the segmented MNs and the reconstructed LMC and MMC of P1 ChAT-eGFP. Related to Figure 1. 49851735908310.mp4 Video S3.The 3D image rendering of the sparsely labeled MNs using AdV, the automatically segmented MNs as well as the manually traced distal dendrites. Related to Figure 5. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5758234","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":397283809,"identity":"453a52b0-75c7-40a3-aae7-fb4b3e8bbc90","order_by":0,"name":"Huijie Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBACAwYeBoYEHgkGfjC3ACgABBJEaZFsAPIOGBCrBcw4QKwWc4ncYxIPZCzkjG8kH/78wcDOmJ+B+eBtHga7PFxaLGfkpUkAHWZsdiMtTeKAQbKZZANbsjUPQ3IxTofdyDEDaUncdjvHDOgwZhuDAzxm0jwMBxIbCGip3zw7//OHAwb1NvYH+L8RpSXBQDqHAeiww2bAAGHDr+XMG2MLoBbDGfefmUmcMThuLHGYzdhyjkEybi3Hcwxv/uypk+fvOfz4Q0VFtWF/e/PDG28q7HBqAQPGHmQeM9gofOpB4AchBaNgFIyCUTCiAQAfFFDISG1YJQAAAABJRU5ErkJggg==","orcid":"","institution":"Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, 310024, China","correspondingAuthor":true,"prefix":"","firstName":"Huijie","middleName":"","lastName":"Hu","suffix":""},{"id":397283810,"identity":"f5851e63-a80d-4033-982f-b539bbe6c23c","order_by":1,"name":"Dongyue Wang","email":"","orcid":"","institution":"Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, 310024, China","correspondingAuthor":false,"prefix":"","firstName":"Dongyue","middleName":"","lastName":"Wang","suffix":""},{"id":397283811,"identity":"c6a18ec9-177f-4e4b-bbeb-dee2e17c783f","order_by":2,"name":"Yanlu Chen","email":"","orcid":"","institution":"Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, 310024, China","correspondingAuthor":false,"prefix":"","firstName":"Yanlu","middleName":"","lastName":"Chen","suffix":""},{"id":397283812,"identity":"c5f74a52-82b2-41e1-8687-0b3850241f7f","order_by":3,"name":"Liang Gao","email":"","orcid":"","institution":"Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, 310024, China","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2025-01-03 12:27:56","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-5758234/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5758234/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73090147,"identity":"bdeb349b-cb7c-4c26-abc6-8dd3528d65e7","added_by":"auto","created_at":"2025-01-06 15:23:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1167703,"visible":true,"origin":"","legend":"\u003cp\u003eThe semi-automatic cell segmentation workflow utilizing the deep learning algorithm. (a) Grayscale image of a small 3D block with a data size of approximately 30 MB, with a scale bar of 200 μm. (b) A single slice extracted from (a). (c) Grayscale image after processing with the \u003cem\u003eMedian Filter\u003c/em\u003e command. (d) Automatically extracted soma boundaries obtained using the \u003cem\u003eHysteresis Thresholding\u003c/em\u003e command. (e) Soma boundaries predicted by the deep learning-trained model, with a scale bar of 500 μm. (f) Grayscale image after the \u003cem\u003eImage Gradient\u003c/em\u003e command processing. (g) Soma segmentation achieved through the \u003cem\u003eMarker-based Watershed inside Mask\u003c/em\u003ecommand. (h, i) Soma boundaries in the 3D image predicted by the deep learning-trained model, with a scale bar of 500 μm. (j) 3D rendering of the soma boundaries, with a scale bar of 500 μm.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5758234/v1/d99ef7c1a0b0674b948008f8.png"},{"id":73089097,"identity":"29ee2164-27f0-456d-ad96-9a80fde77d59","added_by":"auto","created_at":"2025-01-06 15:15:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":566455,"visible":true,"origin":"","legend":"\u003cp\u003eReconstructed MMC and LMC neurons of P1 ChAT-eGFP. (a) The frontal view of the reconstructed MMC and LMC neurons of P1 ChAT-eGFP, with a scale bar of 500 μm. The red surface represented the reconstructed MMC, and the deep blue surface represented the reconstructed LMC. (b) Grayscale image of cervical cord (top), and the grayscale image of lumbar cord (bottom), with a scale bar of 200 μm. (c) Reconstructed LMC and MMC neurons of cervical cord (top), and lumbar cord (bottom), with a scale bar of 200 μm. (d) The transverse plane view of C5-T1 (top), and the transverse plane view of L1-L6 (bottom), with a scale bar of 200 μm. (e) The transverse plane view of C5-T1 (top), overlaid with the reconstructed MMC (depicted in red) and LMC (depicted in deep blue) neurons, with a scale bar of 200 μm. The transverse plane view of L1-L6 (bottom), overlaid with the reconstructed lumbar MMC (depicted in red) and LMC (depicted in deep blue), with a scale bar of 200 μm.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5758234/v1/b60e4a655464d292cd30e634.png"},{"id":73089103,"identity":"c5c1c2c0-d09e-4a81-98c4-424a5eb956f0","added_by":"auto","created_at":"2025-01-06 15:15:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":339600,"visible":true,"origin":"","legend":"\u003cp\u003eHistograms of the soma size distribution of MNs. (a) Histograms of the soma size distribution of MNs in the cervical MMC of P1, P7, P14, P28, and P56 ChAT-eGFP. (b) Histograms of the soma size distribution of MNs in the lumbar LMC of P1, P7, P14, P28, and P56 ChAT-eGFP. (c) Histograms of the soma size distribution of MNs in the lumbar MMC of P1, P7, P14, P28, and P56 ChAT-eGFP. (d) Histograms of the soma size distribution of MNs in the lumbar LMC of P1, P7, P14, P28, and P56 ChAT-eGFP. The volume distribution histograms of MNs are analyzed using Gaussian fitting. The volume range is 0-38,000 µm³, with intervals of 1,000 µm³. n = 1 animal per group.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5758234/v1/3bbd64748064042a6827f5e1.png"},{"id":73090150,"identity":"6ed1e208-3af6-4f99-bf08-99c36ffd7bfe","added_by":"auto","created_at":"2025-01-06 15:23:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":519695,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution pattern of putative γMNs and αMNs in the cervical cord. (a) Grayscale image of the cervical MNs of P56 ChAT-eGFP, with a scale bar of 500 μm. (b) Spatial distribution pattern of the putative γMNs and αMNs of the cervical LMC. (c) A magnified view of two regions in (c). (d) Spatial distribution pattern of the putative γMNs and αMNs of the cervical MMC. (e) A magnified view of two regions in (d).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5758234/v1/512c2202d3b7cdf63a540e96.png"},{"id":73089100,"identity":"dbc0aff9-bbbb-438b-8b58-481a3f202e57","added_by":"auto","created_at":"2025-01-06 15:15:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":700566,"visible":true,"origin":"","legend":"\u003cp\u003eThe semi-automatic method for dendritic tree extraction. (a) Grayscale image of P14 TA MNs retrogradely labeled by AdV, with a scalebar of 200 μm. (b) Dendritic structure was enhanced by \u003cem\u003eStructure Enhancement Filter (Rod Model)\u003c/em\u003ecommand. (c) Somatic structure was enhanced by the \u003cem\u003eStructure Enhancement Filter (Ball Model)\u003c/em\u003e command. (d) The \u003cem\u003eInteractive Thresholding\u003c/em\u003ecommand was employed to transform grayscale images into binary image. (e) The \u003cem\u003eMarker-based Watershed\u003c/em\u003e \u003cem\u003einside Mask\u003c/em\u003e command assigned distinct labels to each MN. (f) The \u003cem\u003eCenterline Tree\u003c/em\u003e command generated the dendritic skeleton. (g) Manual tracing and editing were performed with the \u003cem\u003eFilament Editor\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5758234/v1/136b8ff1ddfd4067c9b51889.png"},{"id":73090888,"identity":"573c67b1-f046-4db6-a0aa-0ea873500510","added_by":"auto","created_at":"2025-01-06 15:31:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":735769,"visible":true,"origin":"","legend":"\u003cp\u003eQuantitative analysis of the dendritic structures of the TA and GL MNs at P4, P14 and P56. (a) 3D surface reconstruction of a single TA MN, obtained using the Amira software, with a scalebar of 200 μm. (b) Skeletonize of a single TA MN, with dendritic branches color-coded by branch order. (c) Tortuosity of a single dendritic branch of a TA MN. The magenta line represents the chord length, and the red arrows indicate the 3D length of the dendritic branch. (d) 3D surface reconstruction of a single GL MN, obtained using the Amira software, with a scalebar of 200 μm. (e) Skeletonize of a single GL MN, with dendritic branches color-coded by branch order. (f) Schematization of tortuosity of a single dendritic branch of a GL MN. (g) Quantitative comparison for Sholl analysis of TA (top) and GL (bottom) MNs at P4, P14, and P56. (h) The number of crossings of each branch of TA (top) and GL (bottom) MNs with concentric circles for each branch order at P4, P14 and P56. (i) The mean length of the first six dendritic orders of TA (top) and GL (bottom) MNs at P4, P14, and P56. (j) The tortuosity of the first six dendritic orders of TA (top) and GL (bottom) MNs at P4, P14, and P56. All data are presented as mean ± SEM. p-values were obtained from two-way ANOVA with Tukey’s multiple comparisons test. Significance levels are indicated as follows: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001. Error bars represent standard error of mean. n = 3 neurons, from two animals.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5758234/v1/8d078d6f96fee226de222fe0.png"},{"id":73091329,"identity":"ab27b09d-d93d-48fb-ae34-ce433fd4b532","added_by":"auto","created_at":"2025-01-06 15:39:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4588152,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5758234/v1/f1f3d070-24fe-432e-9bd4-474ef3e8161a.pdf"},{"id":73089107,"identity":"1be18da8-6e84-4209-a102-67903756e698","added_by":"auto","created_at":"2025-01-06 15:15:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6789120,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental information\u003c/p\u003e","description":"","filename":"DocumentS1cellregeneration.docx","url":"https://assets-eu.researchsquare.com/files/rs-5758234/v1/6dbe47ac6037d02bc7c32836.docx"},{"id":73090149,"identity":"42cd8869-d6ca-4974-a579-c2887ae2fdec","added_by":"auto","created_at":"2025-01-06 15:23:39","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10172,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1.\u003c/strong\u003e The parameter settings of the deep learning command for soma segmentation.\u003c/p\u003e","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5758234/v1/4c34153d3886a06a71945411.xlsx"},{"id":73089119,"identity":"82cb76e1-9f1e-4e87-9946-6457b298ab13","added_by":"auto","created_at":"2025-01-06 15:15:40","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":316863,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S2. \u003c/strong\u003eThe soma size (volume3d) of MNs in each motor column of cervical and lumbar cord at P1, P7, P14, P28 and P56.\u003c/p\u003e","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5758234/v1/4b8950def6fb08554b0e1c7a.xlsx"},{"id":73089116,"identity":"bf55dd3b-d367-47c5-b8d6-445a5ef20963","added_by":"auto","created_at":"2025-01-06 15:15:40","extension":"mp4","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":640216,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVideo S1. \u003c/strong\u003eThe 3D image rendering of the MMC and LMC columns with a clear boundary of P56 ChAT-eGFP. Related to Figure 1.\u003c/p\u003e","description":"","filename":"49831735908282.mp4","url":"https://assets-eu.researchsquare.com/files/rs-5758234/v1/fc18ed8c6ec0396e13eb2b0d.mp4"},{"id":73089114,"identity":"f5772deb-5b2a-49da-b49b-a4303afc057d","added_by":"auto","created_at":"2025-01-06 15:15:40","extension":"mp4","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1515720,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVideo S2. \u003c/strong\u003eThe 3D image rendering of the cervical cord with the segmented MNs and the reconstructed LMC and MMC of P1 ChAT-eGFP. Related to Figure 1.\u003c/p\u003e","description":"","filename":"49841735908295.mp4","url":"https://assets-eu.researchsquare.com/files/rs-5758234/v1/3d44f507229c106b6bcdd633.mp4"},{"id":73089125,"identity":"826c1e37-992d-4d83-ba6e-c9936161c8d2","added_by":"auto","created_at":"2025-01-06 15:15:41","extension":"mp4","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1465553,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVideo S3.\u003c/strong\u003eThe 3D image rendering of the sparsely labeled MNs using AdV, the automatically segmented MNs as well as the manually traced distal dendrites. Related to Figure 5.\u003c/p\u003e","description":"","filename":"49851735908310.mp4","url":"https://assets-eu.researchsquare.com/files/rs-5758234/v1/bd9b0e1377f4e7525d4adb98.mp4"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMorphological segmentation with tiling light sheet microscopy to quantitatively analyze the three-dimensional structures of spinal motoneurons\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eAll motor commands, whether voluntary or involuntary, are ultimately integrated by spinal motoneurons (SpMNs) (Arber and Costa, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Arber, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). SpMNs receive neural projections from descending pathways, spinal interneurons, and peripheral sensory inputs (C\u0026ocirc;t\u0026eacute; et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; de Carvalho and Swash, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), transmitting all signals to muscle fibers via neuromuscular junctions (Stifani, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Motor behaviors depend on the coordinate recruitment of different muscle groups, each activated by a specialized set of SpMNs (Agalliu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). A distinct anatomical correlation exists between SpMNs and muscle fibers, enabling the temporal and spatial synchronization of muscle groups under the control of spMNs (Kernell, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo facilitate the production of varying levels of force output in response to diverse movement scenarios, motor pools typically comprise three distinct types of SpMNs (Bączyk et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dasen, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Simon et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Kernell, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Stifani, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Establishment of cellular diversity during development is essential for SpMNs to execute precise movements. Over the past decades, transcription factor and epigenetic-mediated MN development has been extensively studied in vivo (Agalliu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ashrafi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Liau et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mendelsohn et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dasen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Bulajić et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, anatomical studies of MNs during spinal cord development were limited to 2D histological assessment (Kanjhan et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016b\u003c/span\u003e; Kanjhan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e; Fukuda et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It remains unclear how different types of MNs are organized within the spinal cord and how the spatial organization changes during development.\u003c/p\u003e \u003cp\u003eAs regards imaging, several fluorescence microscopy techniques are available for obtaining volumetric data of whole tissues (Zhong et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pesce et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, while the acquisition speed is an inherent limitation for imaging large-volume tissues, tiling light sheet microscope (TLSM) overcomes this by enabling rapid acquisition of cleared samples. TLSM enables high throughput 3D imaging of centimeter-scale cleared and expanded biospecimens with spatial resolutions ranging from microns to sub-hundred nanometers (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The combination of tissue clearing and TLSM techniques provided a powerful tool for investigating 3D morphological diversity and spatial organization of MNs during postnatal development.\u003c/p\u003e \u003cp\u003eMNs are organized hierarchically, with distinct columns aligned along the rostrocaudal axis of the spinal cord, each targeting specific peripheral structures(Tosney et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). The median motor column (MMC) and lateral motor column (LMC) neurons are essential for mice, as they coordinate axial muscle activity to maintain posture and balance while enabling precise limb movements critical for locomotion and other motor behaviors (Patani, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nicolopoulos-Stournaras and Iles, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Gutman et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Dasen, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Here, we conducted quantitative analysis of the changes in 3D soma size of MMC and LMC neurons in the cervical and lumbar cord at five ages (P1, P7, P14 and P56).\u003c/p\u003e \u003cp\u003eIn addition, dendrites integrate synaptic inputs while minimizing metabolic costs by extending to existing or potential synaptic targets, making the understanding of the dendritic arborization of spinal motoneurons essential (Stuart and Spruston, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Magee, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Sparse labeling is an important aspect of tissue preparation for dendrite tracing and analysis (Bloss et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Over the last two decades, adenovirus (AdV) vectors have become powerful tools for labeling single neuron and treating diseases of central nervous system (CNS) (Salinas et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Andrew Paul Tosolini and Morris, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e). In this study, we found that adenoviral injections produced robust fluorescent expression in MNs with high spatiotemporal resolution, effectively labeling retrogradely. We used AdV via intramuscular injection to label MNs innervating paired flexor and extensor muscles at P4, P14, and P56.\u003c/p\u003e \u003cp\u003eAccurate 3D quantification of soma and dendrites is essential for understanding the normal and pathological neuronal function of MNs. However, traditional quantification has relied on slow, labor-intensive methods like manual counting, which are low-throughput and unsuitable for large samples (Herculano-Houzel and Lent, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Fukuda et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Specifically, manual editing will vary depending on the complexity of the image data. This article presented semi-automatic segmentation methods for objective morphological analysis by providing image acquisition parameters with Amira. These protocols will serve as a valuable reference for scientists aiming to quantify and characterize neural structures in the CNS.\u003c/p\u003e \u003cp\u003eCollectively, we imaged the MMC and LMC neurons in the cervical and lumbar cord during postnatal development as well as MNs inverting paired flexor and extensor muscles. We further presented protocols for 3D volumetric analysis of soma size and dendritic arborization. These morphological analyses offer valuable insights into the diversity of MNs during postnatal development.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSoma segmentation with Amira\u003c/h2\u003e \u003cp\u003eThe TLSM was employed in conjunction with the CUBIC-L tissue clearing method (Matsumoto et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to image the spinal cord of ChAT-eGFP mice at five stages (P1, P7, P14, P28, and P56). A more detailed description of the alignment and operation of TLSM can be found in the previous publication (Feng et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Register and merge the images from adjacent sample volumes with Amira (Feng et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The high density of MNs in the spinal cord of young mice (e.g., at P1 and P7) presents a challenge for automated single-cell segmentation due to the minimal spacing between MNs. To address this issue, we developed a semi-automated soma segmentation workflow based on deep learning algorithms using the commercial software, Amira. All operations were performed using Amira commands. The \u003cem\u003eExtract Subvolume\u003c/em\u003e command was used to extract a small image block with a data size of approximately 30 megabytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Then, we used the \u003cem\u003eMedian Filter\u003c/em\u003e command to erode the signal in the dendrites and axons of MNs. The filter can remove tiny structures, like dendrites and axons, without affecting the boundaries of large components, such as somas (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Next, the \u003cem\u003eHysteresis Thresholding\u003c/em\u003e command was used to convert the grayscale image into a binary image, represented by a blue mask (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). However, as shown by the red arrows in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, this process failed to accurately extract every soma. To enhance the accuracy of soma region extraction, a deep learning command (\u003cem\u003eDL Training-Segmentation 3D\u003c/em\u003e) was employed. The parameter settings of deep learning command were presented in Table S1. The deep learning-trained model was used to predict the soma regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee) in the original grayscale image, achieving greater accuracy compared to the extraction results obtained using the \u003cem\u003eHysteresis Thresholding\u003c/em\u003e command. The \u003cem\u003eImage Gradient\u003c/em\u003e command facilitated the delineation of boundaries between the adjacent MNs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ei demonstrated the application of the deep learning-trained model for predicting soma in 3D samples, with the red mask indicating the predicted soma contours. Additionally, the 3D reconstructed soma mask was shown in Figure j. Figure S1 illustrated the detailed workflow for counting the number of MNs, while the signals extracted therein serve as seeds in the subsequent watershed segmentation procedure, enabling comprehensive and accurate segmentation. We next applied the \u003cem\u003eMarker-based Watershed inside Mask\u003c/em\u003e command to segment individual MN. Each MN was labeled by unique random color, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg. The parameters of the above commands were provided in Figure S2 for reference.\u003c/p\u003e \u003cp\u003eThe generation of accurate soma masks is the most critical step in the whole semi-automated soma segmentation workflow. Traditional threshold segmentation struggles to accurately extract soma regions from the entire 3D image due to the high density of MNs in ChAT-eGFP mice and the complexity of dendritic branching. The deep learning module in Amira software significantly enhanced the accuracy and efficiency of soma boundary extraction. In addition, as the spinal cord was expanded by ~\u0026thinsp;1.2 times in each dimension after clearing, the real soma size was 1.728 (1.2\u003csup\u003e3\u003c/sup\u003e) times smaller than the calculated soma size.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSoma size changes of MMC and LMC neurons during postnatal development\u003c/h3\u003e\n\u003cp\u003eMN subtypes are distinguished by their anatomical position, functional properties, connection specificity, and molecular profiles (Dasen, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The diversity of MNs is a functional necessity for the proper execution and control of movement during postnatal development. Mature MNs exhibit variations in soma size. It is not feasible to differentiate MNs based solely on soma size. However, the observed differences in soma size among MNs can be utilized as a supplementary indicator of differentiation. In previous studies, the soma size of MN was typically assessed by measuring the maximum cross-sectional area of MN in spinal cord slice. Changes in the 3D soma size and the spatial distribution of MN subtypes within the spinal cord during postnatal development remain poorly characterized.\u003c/p\u003e \u003cp\u003eWe identified LMC neurons in the cervical (C5\u0026ndash;T1) and lumbar (L1\u0026ndash;L6) cord regions by correlating vertebral anatomy with spinal cord segments in mice (Figure S3) (Stifani, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dasen and Jessell, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Harrison et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). MMC neurons in these regions were also included in the analysis. The MMC and LMC columns were identified by their spatial locations within the spinal cord, and their distinct boundaries were clearly revealed through 3D image rendering (Video S1). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-c and Video S2 display the cervical and lumbar spinal cord of a P1 ChAT-eGFP mouse, along with the reconstructed LMC and MMC neurons. The transverse planes of these two regions were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed. The top was the cervical region, and the bottom was the lumbar region. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee presented the transverse planes of the reconstructed LMC and MMC neurons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe volumetric changes of MNs during postnatal development were characterized by quantifying the soma size of ChAT-eGFP mice at five ages (P1, P7, P14, P28, P56). A total of 1,049 (P1), 870 (P7), 889 (P14), 783 (P28), and 945 (P56) MNs were reconstructed in the cervical MMC. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea showed the histograms of soma size distributions in the cervical MMC of P1, P7, P14, P28, and P56 ChAT-eGFP mice. Similarly, 3,798 (P1), 3,688 (P7), 3,704 (P14), 3,516 (P28), and 3,553 (P56) MNs were reconstructed in the cervical LMC of ChAT-eGFP. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb demonstrated the histograms of soma size distributions in the cervical LMC of P1, P7, P14, P28, and P56 ChAT-eGFP mice. In the lumbar MMC, 498 (P1), 382 (P7), 375 (P14), 504 (P28), and 594 (P56) MNs were reconstructed. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec depicted the histograms of soma size distributions in the lumbar MMC of P1, P7, P14, P28, and P56 ChAT-eGFP mice. A total of 2,898 (P1), 2,482 (P7), 2,463 (P14), 2,762 (P28), and 2,750 (P56) MNs were reconstructed in the lumbar LMC. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed presented the histograms of soma size distributions in the lumbar LMC of P1, P7, P14, P28, and P56 ChAT-eGFP mice. The soma size (volume3d) of MNs in each motor column during postnatal development were presented in Table S2.\u003c/p\u003e \u003cp\u003eIn the cervical MMC of P1 and P7 mice, the soma size distribution histogram exhibited a single peak. By P14, this distribution evolved to display two distinct peaks, corresponding to the differentiation of MNs into two subgroups. To analyze the soma size distribution histograms of the two MN subgroups, we applied the Gaussian fitting algorithm in Origin software to fit the histogram data for each subgroup. In the P14, P28 and P56 mice, MNs with smaller soma sizes accounted for 32.6%, 34.6% and 35.2%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, indicated by red arrows). Similarly, in the cervical LMC of P1 and P7, the soma size distribution was represented by a single group. At P14, MNs with smaller soma sizes constituted 31.4% of the total population. In P28 and P56 mice, the proportion of smaller-sized MNs was 25.9% and 27.1%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, indicated by red arrows). In the lumbar MMC of P1 to P14 mice, the soma size of MNs was distributed within a single group. In the mice at P28 and P56, MNs with smaller soma sizes presented 28.8% and 30% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, indicated by red arrows), respectively. Additionally, the soma size of MNs in the lumbar LMC of P1 to P14 was represented by a single group. In the P28 and P56 mice, MNs of smaller soma size comprised 31.1% and 31.3% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed, indicated by red arrows), respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSpatial distribution of putative gamma and alpha MNs in the MMC and LMC\u003c/h3\u003e\n\u003cp\u003eFriese et al. employed a combined approach of tissue sectioning and immunofluorescence staining techniques to analyze the maximum cross-sectional area of 800 MNs in the lumbar spinal cord of P21 wild-type mice (Friese et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The researchers identified two distinct populations of MNs: Err3-positive and NeuN-negative MNs (widely recognized as the selective markers for γMNs) and Err3-negative and NeuN-positive MNs (widely recognized as αMNs). The researchers also discovered that Err3-positive and NeuN-negative MNs (putative γMNs) constitute 31% of the total population.\u003c/p\u003e \u003cp\u003eOur observation was in accordance with the findings of Friese et al. We speculated that the subgroup of MNs with smaller soma sizes corresponds to γMNs, while the larger one corresponds to αMNs. We identified the spatial distribution patterns of putative γMNs and αMNs in the mature spinal cord using the results of Gaussian fitting. The x-coordinate of the intersection point between the two fitting curves defined the threshold that separates the putative γMNs (soma size smaller than the threshold) and αMNs (soma size larger than the threshold). In the cervical LMC of P56 ChAT-eGFP, putative γMNs are represented by blue spheres, and putative αMNs were represented by red spheres (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). These spheres were generated through 3D rendering using Amira software. Next, putative γMNs and αMNs in the cervical MMC of P56 ChAT-eGFP were also showed in blue spheres and red spheres (based on Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed. In the same way, the putative γMNs and αMNs in the LMC and MMC of the lumbar cord were represented as blue and red spheres, as shown in Figure S4. Through 3D rendering of the spatial distribution of putative γMNs and αMNs, we observed an interesting phenomenon: although smaller MNs (putative γMNs) account for approximately 30% of the total population, their spatial distribution was not uniformly proportional to this percentage (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). While the spatial distribution pattern of putative γMNs and αMNs was preliminarily predicted through soma reconstruction and 3D rendering, confirming these observations with immunohistochemical analyses (e.g., detecting Err3 and NeuN) remains a key step for future exploration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDendrites extraction with Amira\u003c/h3\u003e\n\u003cp\u003eIn order to investigate the dendritic arborization patterns of MNs at the single-cell level during development, we retrogradely labeled the MNs innervating the tibialis anterior muscle (TA, flexor) and the gastrocnemius lateralis muscle (GL, extensor) with AdV at P4, P14, and P56 (Figure S5a\u0026ndash;f). Multiple site injections of AdV-eGFP and AdV-tdTomato were administered along the three-dimensional distribution of each target muscle\u0026rsquo;s motor endplate (Xu et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A mixture of AdV and 1% Fast Green was used to aid in visualization (Figure S5g, top). After 72 hours, the spinal cord was dissected and the fluorescent signals in the target muscles were observed under a fluorescence stereomicroscope. The dissected spinal cord then underwent tissue clearing and was imaged using TLSM (bottom of Figure S5g, 5a). We employed a quantitative analysis of the spatial distribution of MNs innervating TA and GL muscles at P4, P14, and P56. As age increased, MNs were located progressively farther from the lateral and ventral borders of the spinal cord (Figure S5h\u0026ndash;j).\u003c/p\u003e \u003cp\u003eA semi-automatic method for dendritic tree extraction was developed using Amira (Video S3). This method involved reconstructing dendritic arborization and quantifying the number and length of dendrites. First, the \u003cem\u003eStructure Enhancement Filter (Rod Model)\u003c/em\u003e command was used to highlight tubular structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), while the \u003cem\u003eStructure Enhancement Filter (Ball Model)\u003c/em\u003e command was applied to enhance spherical structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Next, the \u003cem\u003eInteractive Thresholding\u003c/em\u003e command converted the grayscale images into binary images, visualized as blue masks (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). Subsequently, the \u003cem\u003eMarker-based Watershed inside Mask\u003c/em\u003e command assigned distinct labels to each MN, with each label displayed in a different color (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). The \u003cem\u003eCenterline Tree\u003c/em\u003e command then extracted the centerline of the segmented MNs, producing the dendritic branch skeleton (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). Finally, manual tracing and editing were performed to ensure the complete and accurate extraction of the distal dendrites (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDendritic branching patterns of MNs that innervate the flexor and extensor muscles at P4, P14 and P56\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe reconstructed the 3D skeleton of dendrites of 3 motor neurons from the TA (Figure S6a) and GL (Figure S6b) motor pools at P4, P14 and P56. The reconstructed neural surface included the soma, dendrites, and axon (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed), with dendritic branches color-coded by branch order using unique random colors for each order (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee). To evaluate changes in dendritic complexity during development, we conducted a Sholl analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed) (Sholl, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1953\u003c/span\u003e), which revealed that the number of intersections between the dendrites and concentric circles initially increased and then decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eg). By integrating the Sholl analysis results with dendritic order information, we found that the region of highest dendritic density was associated with the 4th or 5th dendritic order (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eh). We next quantified the mean length of the first six dendritic orders in both TA and GL MNs at each developmental stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ei). In TA MNs, the mean length of each dendritic branch increased from P4 to P14, with no statistical difference detected between P14 and P56 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ei, top). In contrast, GL MNs displayed a continuous increase in the mean length of each dendritic branch from P4 to P56 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ei, bottom). In addition, it has been demonstrated that dendrites increase their probability of connecting with target synapses by increasing their curvature (Stepanyants et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Rothnie et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). we also measured tortuosity-defined as the ratio of curve length to chord length (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef)- in the first six dendritic orders of TA and GL MNs at P4, P14, and P56. We compared the P1 vs. P14 vs. P56 groups. No statistical differences in tortuosity were observed during development (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ej).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we started with utilizing a combination of TLSM and CUBIC-L to visualize the MNs of ChAT-eGFP mice during postnatal development. The imaging and tissue clearing techniques provided rapid acquisition of large-volume samples with submicron resolution and obviate the need for physically sectioning. Then, we developed a protocol to effectively segment soma from large-volume and high-throughput samples using Amira. Commercial software typically provides standardized processing procedures, which enhance the reproducibility. MNs undergo a differentiation process during development, resulting in the emergence of three subtypes: αMNs, βMNs and γMNs (MANUEL and ZYTNICKI, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This regulation is crucial for the maintenance of muscle tone, the adjustment of muscle reflexes, and fine motor control. Previous studies have employed immunofluorescence and tissue sectioning techniques to analyze the cross-sectional area of αMNs and γMNs. The histogram of the cross-sectional area of the soma revealed the presence of two distinct groups. The smaller group was identified as putative γMNs, constituting approximately 30% of the total (Friese et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Shneider et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Our study revealed that MNs in the cervical and lumbar regions also differentiated into two groups with distinct soma sizes around P14, which was consistent with the results of the aforementioned studies. We speculated that the alterations in MN soma size during development may serve as an indicator of differentiation.\u003c/p\u003e \u003cp\u003ePrevious studies have indicated that the muscle groups of the forelimbs develop slightly earlier than those of the hindlimbs during the initial few days after birth (Zhu and Tabin, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Martin, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). For instance, at P11, the phosphorylation levels of specific proteins, such as Pak1 and Pak2, exhibit disparities between the forelimb and hindlimb muscles (Joseph et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This early developmental advantage endows the forelimbs with greater robustness when neonatal mice commence movement and exploration of their surrounding environment. The present study analyzed the 3D soma size of MNs in the cervical region and revealed that MNs innervating the forelimbs differentiate into two distinct groups with varying soma sizes by P14. However, the differentiation of MNs in the lumbar region was delayed until after P14. This observation indicated that the maturation and differentiation of MNs innervating the hindlimbs occur at a later stage than those innervating the forelimbs.\u003c/p\u003e \u003cp\u003eAdditionally, our findings revealed that the distribution of putative γMNs and αMNs is not uniform. In certain regions, putative γMNs are more abundant, while in others, putative αMNs are more prevalent. Although no further validation was performed, this finding suggested that the proportion of γMNs and αMNs may vary depending on the muscle fibers they innervate. It\u0026rsquo;s practicable to inject retrograde tracers into the target muscle, which could allow for a predictive analysis of the proportion of γMNs and αMNs by examining the soma size. We speculated that the function and influence on the ALS-like pathologies of the particular muscle could be further inferred from the proportion of γMNs and αMNs in the corresponding motor pool. This approach may also provide a theoretical basis for understanding how the motor system efficiently synchronizes various motor pools and for developing more effective exercise strategies.\u003c/p\u003e \u003cp\u003eIt was observed that MNs retrogradely labeled with AdV exhibited robust fluorescent expression, while the distribution of fluorescence signals was uneven. In particular, the fluorescence intensity in the soma region differed by a factor of 2\u0026ndash;3 compared to the apical dendrite region. To address this problem, we employed extensive manual tracing to extract the complete dendritic arborization. The analysis demonstrated that the number of dendritic branches of MNs, regardless of whether they innervate flexors or extensors, tended to increase and then decrease. This indicates that the number of branches is less near the soma and proximal dendrites, with the majority of branches situated in the mid-dendritic region. It is noteworthy that the length of dendrites of MNs increases with age, and the pattern of dendrites constructed by P4 is similar to that of mature adults. The intricate structure of dendrites is primarily a consequence of their function in integrating synaptic inputs while simultaneously minimizing metabolic costs. Furthermore, comprehensive understanding the dendritic structures of MNs can also facilitate the development of efficient mathematical models of neural networks for motor control.\u003c/p\u003e \u003cp\u003eIn summary, this study provided a method for capturing the 3D morphology of MNs during postnatal development by combining TLSM imaging system, CUBIC-L tissue clearing protocol, and AdV labeling. Moreover, this study established highly reproducible image analysis methods to quantitatively analyze the morphological changes of MNs during postnatal development in two aspects: differentiation of soma size and dendritic architecture. The findings provide new insights into the maturation, differentiation, and functional properties of MNs, allowing a deeper understanding of the motor system during development.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe achievement of high-resolution imaging of MNs in the whole spinal cord requires the handling of tens to hundreds of terabytes of data. Despite the establishment of a semi-automatic soma segmentation method based on deep learning, the computational process remains time-consuming and requires high-performance workstations. Consequently, in the initial stages of our investigation, the changes in soma size were restricted to the cervical and lumbar LMC and MMC of a single mouse per age group. This limitation may affect the generalizability of our findings, as the data from a single mouse may not fully represent the variability and complexity observed in a broader population. Besides, how can γMNs be differentiated before the clear bifurcation of their soma size into two distinct groups? This question cannot be answered on the basis of morphological changes alone. Further explorations involving specific molecular markers and electrophysiological properties of αMNs, βMNs and γMNs are required. Over the past decade, numerous molecular markers for γMNs have been identified (Zuccaro et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). However, it should be noted that many of these markers can be expressed in both αMNs and βMNs. Combing multiple molecular markers is essential.\u003c/p\u003e \u003cp\u003eIn examining the dendritic arborization of MNs, the TA and GL, which are superficial muscles, were labeled. The present study did not examine the dendritic branching patterns of MNs that innervate deeper muscles. This introduces a limitation, as the structural and functional characteristics of MNs may differ between the superficial and deep muscles. Furthermore, the intricate and compact structure of MN distal dendrites, necessitated substantial manual calibration to ensure accurate segmentation. The labor-intensive nature of this process resulted in a reduction in the overall throughput of data analysis, thereby limiting the number of MNs that could be selected for dendrite analysis to only three per age group.\u003c/p\u003e \u003cp\u003eFurthermore, the dendrite tracing approach only extracted the centerline of the dendrites. The centerline extraction algorithm of Amira identifies the highest intensity path within a dendrite, rather than capturing the full grayscale signal present in the original image. As a result, this method does not yield information regarding the diameter, surface area, and volume of a dendrite. The diameter of a dendrite has a direct effect on its electrical conductivity. Measuring the diameter of a dendrite requires the application of additional analysis algorithms.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe collection of mice tissues used in this study was approved by the Institutional Animal Care and Use Committee of the Westlake University (Approval No: 19-035-GL).\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by Westlake Education Foundation, the National Natural Science Foundation of China (32150015) and the Zhejiang Province Natural Science Foundation (LR20C070002).\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eLG and HH designed this project; LG and DW built microscopy system; HH and YC conducted tissue clearing and imaging; HH conducted tracer injection and data analysis; HH wrote the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe thank Yongdeng Zhang and Fengquan Zhou for helpful discussions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgalliu, D., Takada, S., Agalliu, I., McMahon, A. 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All mouse lines were kept on C57BL/6J background and were housed in a standard 12:12 light-dark cycle.\u003c/p\u003e\n\u003cp\u003eC57BL/6J was purchased from Shanghai Jihui Laboratory Animal Care Co., Ltd..ChAT-eGFP (B6.Cg-Tg(RP23-268L19-EGFP)2Mik/J, Stock No: 007902) was kindly provided by Dr. Liang Wang from Zhejiang University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTracer preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn expression cassette comprising the cytomegalovirus promoter and the cDNA encoding enhanced green fluorescent protein (eGFP) packaged into adenovirus serotype 5 was purchased from OBiO Technology（Shanghai）Corp., Ltd.. The viral titer was 1.58 × 10\u003csup\u003e11\u003c/sup\u003e pfu/ml. The solutions were stored at -80℃ until use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntramuscular injections\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe location of injection sites based on the 3D distributions of motor endplate in the tibialis anterior and gastrocnemius lateralis, according to the published literatures (Xu et al., 2021; A. P. Tosolini and Morris, 2016b). The mice were deeply anaesthetized, and the right hind limb was secured with medical tape, shaving the tibialis anterior and gastrocnemius lateralis muscle regions. In the mice at P1, three sites were injected with an injection depth of 0.1-0.2 mm; In the mice at P11, five sites were injected with an injection depth of 0.3-0.6 mm; In the mice at P51, contained seven injection sites with an injection depth of 0.6-1.2 mm. Each site was injected with 0.5 μl of AdV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpinal cord harvest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter the intramuscular injections, the mice were kept for 3 days to allow for optimal retrograde transport of neuronal tracer. Then, mice were deeply anaesthetized and perfused with 37℃ saline followed by 4℃ 4% paraformaldehyde. The mouse spinal cord was dissected following a previously reported protocol (Kennedy et al., 2011). The dissected spinal cord was loosely wrapped with a 200 μm Nylon mesh and bound to a perforated Teflon plate with surgical sutures so that the spinal cord can maintain the straight shape through the sample preparation process. The bound spinal cord was placed in 4% PFA overnight at 4°C with gentle shaking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreparation of cleared and expanded mouse spinal cord samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe preparation of cleared spinal cord samples was referenced from CUBIC-L protocol (Matsumoto et al., 2019; Tainaka et al., 2018). Preparation of the cleared spinal cord: First, the fixed spinal cord was immersed in the delipidation solution (10 wt% N-butyldiethanolamine, 10 wt% Triton X-100 and 80 wt% dH2O) at 37 °C for three days with gentle shaking. The delipidation solution was replaced every 24 hours. Next, the delipidated spinal cord was RI matched by immersing the spinal cord in the RI matching solution of RI ~1.49 (25 wt% urea, 22.5 wt% sucrose, 22.5 wt% antipyrine, 10 wt% triethanolamine) at 25°C for two days with gentle shaking. The RI matching solution was replaced every 24 hours. Finally, the RI matched spinal cord was embedded in 2% agarose gel made with the RI matching solution. The cleared and embedded spinal cord was mounted on the sample holder by attaching the sample to the magnets of the sample holder and imaged with the imaging system using Silicone oil as the imaging buffer (Feng et al., 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicroscopy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe configuration of our imaging system was described in our previous publications (Chen et al., 2020). It is capable of imaging centimeter-scale cleared tissues with micron-scale to submicron-scale spatial resolution by using real-time optimized tiling light sheets and detective objectives with various numerical apertures (Olympus XLSLPLN10XSVMP, Nikon Plan Apo 10x Glyc, or Nikon CFI90 20XC Glyc). The resolving ability can be improved to ~70 nm in combined with tissue expansion. The microscope conducts multicolor imaging sequentially for up to 5 colors with the excitation wavelengths of 405 nm, 488 nm, 515 nm, 561 nm, and 638 nm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImage processing, registration and merging protocols were described in our previous publications (Feng et al., 2021). Soma segmentation and dendrite tracing were conducted using Amira semiautomatically, and the methodologies are described in detail in Figure 1 (soma segmentation) and 5 (dendrite tracing).\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"59491f9c-b639-470f-b6f2-3001a6c471e5","identifier":"10.13039/501100001809","name":"National Natural Science Foundation of China","awardNumber":"32150015","order_by":0},{"identity":"0132e960-2d64-4672-b60f-f58d55cb9ec5","identifier":"10.13039/501100004731","name":"Natural Science Foundation of Zhejiang Province","awardNumber":"LR20C070002","order_by":1}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Zhejiang University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tiling light sheet microscopy, Tissue clearing methods, Spinal motoneurons, 3D volumetric reconstruction, Soma size, Dendritic arborization","lastPublishedDoi":"10.21203/rs.3.rs-5758234/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5758234/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSpinal motoneurons control muscle fibers contraction and drive all motor behaviors in vertebrates. Although spinal motoneurons share the fundamental role of innervating muscle fibers, they exhibit remarkable diversity that reflects their specific identities. Defining the morphological changes during postnatal development is critical for elucidating this diversity. However, our understanding of the three-dimensional (3D) morphology of spinal motoneurons at these stages remains limited, largely due to the lack of high-throughput imaging tools. Using tiling light sheet microscopy combined with tissue clearing methods, we imaged motoneurons of the lateral and median motor column in the cervical and lumbar cord during postnatal development. By analyzing their soma size, we found that motoneurons innervating the upper limbs differentiate into two subpopulations with distinct soma size by postnatal day 14 (P14), while differentiation of motoneurons innervating the lower limbs is delayed. Furthermore, coupling adenovirus labeling with 3D volumetric reconstruction, we traced and measured the number and lengths of dendrites of flexor and extensor motoneurons in the lumbar cord, finding that the number of dendrites initially increases and subsequently declines as dendritic order rises. Together, these findings provide a quantitative analysis of the 3D morphological changes underlying spinal motoneuron diversity.\u003c/p\u003e","manuscriptTitle":"Morphological segmentation with tiling light sheet microscopy to quantitatively analyze the three-dimensional structures of spinal motoneurons","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-06 15:15:34","doi":"10.21203/rs.3.rs-5758234/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07f1a583-f846-4767-9304-e3835552e3bb","owner":[],"postedDate":"January 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42328586,"name":"Cellular \u0026 Molecular Neuroscience"}],"tags":[],"updatedAt":"2025-01-06T15:15:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-06 15:15:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5758234","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5758234","identity":"rs-5758234","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}